This study examined the accuracy for author identification by text mining. We conducted 16 analyses (four writing styles × four multivariate analyses) across texts of 100 Bloggers, written by approximately 1,000 characters. Specifically, we conducted (1) principal components analysis, (2) correspondence analysis, (3) multi-dimensional scaling, and (4) hierarchical cluster analysis on each writing style: (1) rate of usage of non-independent words, (2) bigram of parts-of-speech, (3) bigram of postpositional particles, and (4) positioning of commas. We obtained high accuracy: 100% on sensitivity and 95.1% on specificity. Furthermore, the results showed no effects of age and gender against accuracy for author identification.